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Re: clutch

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  • harlanzo@yahoo.com
    When considering clutch it seems weird to think about players actually improving over how they would in normal (nonpressure) situations. Rather, it seems to
    Message 1 of 19 , Dec 2, 2001
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      When considering clutch it seems weird to think about players
      actually improving over how they would in normal (nonpressure)
      situations. Rather, it seems to me that we might better define
      clutch by looking at who did not become worse in clutch situations.
      How you define clutch situations, incidentally, is a question I can't
      really answer.

      --- In APBR_analysis@y..., Ed Weiland <weiland1029@y...> wrote:
      >
      > --- "Michael K. Tamada" <tamada@o...> wrote:
      > >
      > > I would add that the notion that Mike Goodman and
      > > others have advocated,
      > > of looking at playoff games as clutch situations, is
      > > I think a good one,
      > > and the fact that West's FG% was as high in the
      > > playoffs as it was in the
      > > regular season is in itself a fairly remarkable, one
      > > might even say
      > > clutch, performance. Especially given that his
      > > scoring per game INCRASED.
      >
      >
      > Increased shooting could also be a case of a player
      > trying to shoulder too much of the load. It's
      > interesting that in West's case the season his team
      > finally broke through and won the championship, 1972,
      > was the only year he averaged fewer points in the
      > playoffs than the regular season. West also shot only
      > .376 during the 1972 playoffs, by far the worst
      > showing of his career. He did post a career playoff
      > high in assists per game during the '72 playoffs.
      >
      > Here are some other championship performances:
      >
      > Wilt in '67 averaged a then career-low 21.7 ppg in the
      > playoffs, shot 104 points below his regular season FG
      > pct. (albeit a more-than-adequate .579), but posted a
      > career high with 9.0 assists per game.
      >
      > Hakeem in '94 and '95 had FG pct. similar to his
      > regular season and career totals, but posted two of
      > his three highest playoff assist per game totals, 4.5
      > and 4.3 apg, both well above his career playoff
      > average of 3.3. Hakeem scored 33.0 ppg in the '95
      > playoffs, so it's not like he was sacrificing his
      > shots.
      >
      > I'm not sure if the spike in assists is most
      > responsible for the championships, but I don't think
      > it can be ignored. Especially considering that star
      > players who aren't point guards, but possessed
      > good-to-great passing skills like Russell, Barry,
      > Walton, Bird and Jordan tended to win championships.
      > Sometimes the the most clutch thing for a player to do
      > is to get his teammates involved.
      >
      > btw, I don't mean to knock West as non-clutch. HIs
      > Laker teams lost three game sevens to the Celtics by a
      > total of seven points. There had to be some bad luck
      > involved in all that.
      >
      > Ed Weiland
      >
      > __________________________________________________
      > Do You Yahoo!?
      > Buy the perfect holiday gifts at Yahoo! Shopping.
      > http://shopping.yahoo.com
    • igor eduardo küpfer
      ... Here s the correlation matrix. (I hope it formats ok.) Days Dist Home MatchupP Dist 0.076 0.000 Home 0.173 -0.249 0.000 0.000
      Message 2 of 19 , May 30, 2004
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        Dean Oliver wrote:
        > Ed --
        >
        > Nice. Is there correlation between variables? One that is key to
        > understand is whether distance from previous game and days off are
        > correlated. A home stand could be hiding some aspect of time off
        > between games.

        Here's the correlation matrix. (I hope it formats ok.)

        Days Dist Home MatchupP
        Dist 0.076
        0.000

        Home 0.173 -0.249
        0.000 0.000

        MatchupP -0.014 0.021 0.001
        0.509 0.315 0.975

        PtsDiff 0.060 -0.041 0.226 0.465
        0.003 0.048 0.000 0.000

        Cell Contents: Pearson correlation
        P-Value

        The correlations are generally pretty low.

        > (Something also irks me about the p_win variable being
        > endogenous.)
        >

        I'm not quite sure what endogenous means. If it means being related to the
        other variables, I'm not quite sure if that's true: the matchup probability
        calculation uses only team winning percentage and opponent winning
        percentage, neither of which have any relationship to the other variables.
        Maybe I misunderstood.


        > I know I did a study of time off between games and saw that there is
        > an optimal period of time off (more than 2 wasn't good, but neither
        > was 0). That would imply a squared term in days off. But I didn't do
        > it as rigorously as you did.
        >

        I did something like that, too. I can't remember which season I used, but I
        found that most wins came on 2 day rests (I think). However, I didn't
        include any other variables, so I could have just been looking at a
        scheduling quirk for that season. I'll probably rerun this study on another
        season to see if the results hold. If anyone else wants to give it a shot,
        here's a table showing travel distances between NBA cities:

        http://members.rogers.com/brothered/junk/TravelDistances.htm

        ed
      • Dean Oliver
        ... Yeah, pretty low. Probably not much to worry about. ... to the ... probability ... variables. ... Basically, I assume you use 2004 win-loss records to
        Message 3 of 19 , May 30, 2004
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          --- In APBR_analysis@yahoogroups.com, igor eduardo küpfer
          <edkupfer@r...> wrote:
          > Dean Oliver wrote:
          > > Ed --
          > >
          > > Nice. Is there correlation between variables? One that is key to
          > > understand is whether distance from previous game and days off are
          > > correlated. A home stand could be hiding some aspect of time off
          > > between games.
          >
          > Here's the correlation matrix. (I hope it formats ok.)
          >
          > Days Dist Home MatchupP
          > Dist 0.076
          > 0.000
          >
          > Home 0.173 -0.249
          > 0.000 0.000
          >
          > MatchupP -0.014 0.021 0.001
          > 0.509 0.315 0.975
          >
          > PtsDiff 0.060 -0.041 0.226 0.465
          > 0.003 0.048 0.000 0.000
          >
          > Cell Contents: Pearson correlation
          > P-Value
          >
          > The correlations are generally pretty low.
          >

          Yeah, pretty low. Probably not much to worry about.

          > > (Something also irks me about the p_win variable being
          > > endogenous.)
          > >
          >
          > I'm not quite sure what endogenous means. If it means being related
          to the
          > other variables, I'm not quite sure if that's true: the matchup
          probability
          > calculation uses only team winning percentage and opponent winning
          > percentage, neither of which have any relationship to the other
          variables.
          > Maybe I misunderstood.

          Basically, I assume you use 2004 win-loss records to evaluate p_win.
          Well, those win-loss records are built from the things you are looking
          at -- whether a team is at home or on the road, how many days off,
          their whole schedule. Maybe the win-loss records of teams prior to
          the matchup of the game you're looking at is exogenous (known a
          priori). i.e., San Antonio faces the Lakers when one team is 12-5 and
          the other is 10-7 -- use those records rather than their end of season
          records. Maybe that's what you're doing, I dunno. I have doubt that
          it would make a significant difference.


          >
          >
          > > I know I did a study of time off between games and saw that there is
          > > an optimal period of time off (more than 2 wasn't good, but neither
          > > was 0). That would imply a squared term in days off. But I didn't do
          > > it as rigorously as you did.
          > >
          >
          > I did something like that, too. I can't remember which season I
          used, but I
          > found that most wins came on 2 day rests (I think). However, I didn't
          > include any other variables, so I could have just been looking at a
          > scheduling quirk for that season. I'll probably rerun this study on
          another
          > season to see if the results hold.

          Just include the variable Days^2 in your regression and rerun that.
          See what comes out significant.

          DeanO

          Dean Oliver
          Author, Basketball on Paper
          http://www.basketballonpaper.com
          "Oliver goes beyond stats to dissect what it takes to win. His breezy
          style makes for enjoyable reading, but there are plenty of points of
          wisdom as well. This book can be appreciated by fans, players,
          coaches and executives, but more importantly it can be used as a text
          book for all these groups. You are sure to learn something you didn't
          know about basketball here." Pete Palmer, co-author, Hidden Game of
          Baseball and Hidden Game of Football
        • igor eduardo küpfer
          Dean Oliver wrote: ... Ah. I will try to use contemporary win/loss records in my next analysis. ... You ll have to help me out here, as I don t
          Message 4 of 19 , May 30, 2004
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            Dean Oliver wrote:
            <snip>

            >>>
            >>
            >> I'm not quite sure what endogenous means. If it means being related
            > to the
            >> other variables, I'm not quite sure if that's true: the matchup
            >> probability calculation uses only team winning percentage and
            >> opponent winning percentage, neither of which have any relationship
            >> to the other variables. Maybe I misunderstood.
            >
            > Basically, I assume you use 2004 win-loss records to evaluate p_win.
            > Well, those win-loss records are built from the things you are looking
            > at -- whether a team is at home or on the road, how many days off,
            > their whole schedule. Maybe the win-loss records of teams prior to
            > the matchup of the game you're looking at is exogenous (known a
            > priori). i.e., San Antonio faces the Lakers when one team is 12-5 and
            > the other is 10-7 -- use those records rather than their end of season
            > records. Maybe that's what you're doing, I dunno. I have doubt that
            > it would make a significant difference.

            Ah. I will try to use contemporary win/loss records in my next analysis.

            <snip>

            > Just include the variable Days^2 in your regression and rerun that.
            > See what comes out significant.
            >

            You'll have to help me out here, as I don't know anything about transforming
            data. Do you mean include Days^2 in addition to Days or instead of Days? I
            did both, and here's how they turned out:

            PtsDiff = - 21.2 + 2.59 Days +0.000021 Dist + 5.59 Home + 30.0 MatchupP -
            0.394 Days_2

            Predictor Coef SE Coef T P
            Constant -21.169 1.195 -17.71 0.000
            Days 2.5924 0.7956 3.26 0.001
            Dist 0.0000214 0.0003555 0.06 0.952
            Home 5.5937 0.4858 11.51 0.000
            MatchupP 29.991 1.134 26.45 0.000
            Days_2 -0.3938 0.1350 -2.92 0.004

            PtsDiff = - 18.1 +0.000099 Dist + 5.87 Home + 29.9 MatchupP + 0.0236 Days_2

            Predictor Coef SE Coef T P
            Constant -18.0811 0.7304 -24.76 0.000
            Dist 0.0000990 0.0003555 0.28 0.781
            Home 5.8677 0.4794 12.24 0.000
            MatchupP 29.890 1.136 26.32 0.000
            Days_2 0.02362 0.04275 0.55 0.581


            ed
          • Dean Oliver
            ... I should note that, not being an economist, I like throwing this word around without as great an appreciation or understanding for it as I should. ...
            Message 5 of 19 , May 30, 2004
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              --- In APBR_analysis@yahoogroups.com, igor eduardo küpfer
              <edkupfer@r...> wrote:
              > >> I'm not quite sure what endogenous means. If it means being

              I should note that, not being an economist, I like throwing this word
              around without as great an appreciation or understanding for it as I
              should.


              > You'll have to help me out here, as I don't know anything about
              transforming
              > data. Do you mean include Days^2 in addition to Days or instead of
              Days? I
              > did both, and here's how they turned out:

              Include both, which you did in the first set below. Looks like it got
              you significant on both days and days^2. And the signs are as
              expected. It suggests optimal rest at about 3 days, longer than the 2
              days we saw before. (Potentially important for the talk about rust vs
              rest, esp if the Lakers wrap up on M.) Let me also ask -- is Days = 0
              if a team plays back to back nights or is that Days = 1?

              I'm sure there are other ways to manipulate things, but this looks
              like a pretty good thing. I'm saving it.

              Home is a binary 1/0 indicator for home/road, resp?

              >
              > PtsDiff = - 21.2 + 2.59 Days +0.000021 Dist + 5.59 Home + 30.0
              MatchupP -
              > 0.394 Days_2
              >
              > Predictor Coef SE Coef T P
              > Constant -21.169 1.195 -17.71 0.000
              > Days 2.5924 0.7956 3.26 0.001
              > Dist 0.0000214 0.0003555 0.06 0.952
              > Home 5.5937 0.4858 11.51 0.000
              > MatchupP 29.991 1.134 26.45 0.000
              > Days_2 -0.3938 0.1350 -2.92 0.004
              >
              >

              DeanO

              Dean Oliver
              Author, Basketball on Paper
              http://www.basketballonpaper.com
              "Dean Oliver looks at basketball with a fresh perspective. If you
              want a new way to analyze the game, this book is for you. You'll
              never watch a game the same way again. We use his stuff and it helps
              us." Yvan Kelly, Scout, Seattle Sonics
            • igor eduardo küpfer
              Okay, I ran the test again, this time using 03-04 results. Before I show you what I got, let me address a couple of things. ... Hell, that s nothing. Once
              Message 6 of 19 , May 31, 2004
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                Okay, I ran the test again, this time using 03-04 results. Before I show you
                what I got, let me address a couple of things.

                Dean Oliver wrote:
                > --- In APBR_analysis@yahoogroups.com, igor eduardo küpfer
                > <edkupfer@r...> wrote:
                >>>> I'm not quite sure what endogenous means. If it means being
                >
                > I should note that, not being an economist, I like throwing this word
                > around without as great an appreciation or understanding for it as I
                > should.

                Hell, that's nothing. Once during the course of an argument with an
                ex-girlfriend I used the word "heretofore." I still don't know what it
                means.

                >
                >> You'll have to help me out here, as I don't know anything about
                >> transforming data. Do you mean include Days^2 in addition to Days or
                >> instead of Days? I did both, and here's how they turned out:
                >
                > Include both, which you did in the first set below. Looks like it got
                > you significant on both days and days^2. And the signs are as
                > expected. It suggests optimal rest at about 3 days, longer than the 2
                > days we saw before. (Potentially important for the talk about rust vs
                > rest, esp if the Lakers wrap up on M.)

                Questions: I don't understand a couple of things about the squared term. How
                did you know that squaring the Days variable would give a better fit? And,
                just exactly how does it suggest the optimal 3 day rest?

                > Let me also ask -- is Days = 0
                > if a team plays back to back nights or is that Days = 1?
                >

                The latter. I am subtracting game dates from each other.

                > I'm sure there are other ways to manipulate things, but this looks
                > like a pretty good thing. I'm saving it.
                >
                > Home is a binary 1/0 indicator for home/road, resp?

                Yes.

                Okay. Here are the results for 03-04. For the Matchup Probability, I used
                the team records heading into the game. For example, for two teams playing
                their first games of the season, I would use 0-0 records for each team in my
                probability calculation. Interestingly, this doesn't seem to affect the
                regression results too much. The effect of Days between games is reduced in
                this sample. Weird.


                PtsDiff = - 13.6 + 7.31 Home +0.000027 Distance + 18.1 WinProb + 0.722
                Days - 0.122 Days^2

                Predictor Coef SE Coef T P
                Constant -13.582 1.173 -11.58 0.000
                Home 7.3056 0.5010 14.58 0.000
                Distance 0.0000269 0.0003734 0.07 0.943
                WinProb 18.054 1.163 15.53 0.000
                Days 0.7221 0.7202 1.00 0.316
                Days^2 -0.1216 0.1138 -1.07 0.286

                S = 11.48 R-Sq = 16.7% R-Sq(adj) = 16.6%

                Analysis of Variance

                Source DF SS MS F P
                Regression 5 62072 12414 94.19 0.000
                Residual Error 2343 308806 132
                Total 2348 370877

                ed
              • Dean Oliver
                ... show you ... I ve had those moments, often inspired by arguments with soon-to-be ex-girlfriends. What the hell is vis-a-vis ? ... term. How ... fit? And,
                Message 7 of 19 , May 31, 2004
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                  --- In APBR_analysis@yahoogroups.com, igor eduardo küpfer
                  <edkupfer@r...> wrote:
                  > Okay, I ran the test again, this time using 03-04 results. Before I
                  show you
                  > what I got, let me address a couple of things.
                  >
                  > Dean Oliver wrote:
                  > > --- In APBR_analysis@yahoogroups.com, igor eduardo küpfer
                  > > <edkupfer@r...> wrote:
                  > >>>> I'm not quite sure what endogenous means. If it means being
                  > >
                  > > I should note that, not being an economist, I like throwing this word
                  > > around without as great an appreciation or understanding for it as I
                  > > should.
                  >
                  > Hell, that's nothing. Once during the course of an argument with an
                  > ex-girlfriend I used the word "heretofore." I still don't know what it
                  > means.

                  I've had those moments, often inspired by arguments with soon-to-be
                  ex-girlfriends. What the hell is "vis-a-vis"?

                  > >
                  > >> You'll have to help me out here, as I don't know anything about
                  > >> transforming data. Do you mean include Days^2 in addition to Days or
                  > >> instead of Days? I did both, and here's how they turned out:
                  > >
                  > > Include both, which you did in the first set below. Looks like it got
                  > > you significant on both days and days^2. And the signs are as
                  > > expected. It suggests optimal rest at about 3 days, longer than the 2
                  > > days we saw before. (Potentially important for the talk about rust vs
                  > > rest, esp if the Lakers wrap up on M.)
                  >
                  > Questions: I don't understand a couple of things about the squared
                  term. How
                  > did you know that squaring the Days variable would give a better
                  fit? And,
                  > just exactly how does it suggest the optimal 3 day rest?

                  I didn't _know_ it would give a better fit. I hoped it would because
                  of what we were observing -- that there was an optimal number of days
                  off. The only way to get an optimum out of a regression is to throw
                  in higher order terms. Usually a squared term is plenty. It doesn't
                  answer the bigger question of whether teams get rusty, though. It
                  suggests an answer (another lesson in how to lie with statistics), one
                  that I wouldn't trust from this study.

                  Look at the results of your regression. Take just the Days and Days^2
                  coefficients and calculate the marginal net points those terms
                  contribute for Days = 1, 2, 3, 4, etc. You'll see a max at 3.

                  >
                  > > Let me also ask -- is Days = 0
                  > > if a team plays back to back nights or is that Days = 1?
                  > >
                  >
                  > The latter. I am subtracting game dates from each other.
                  >

                  So 2 days of rest is optimal.

                  > > I'm sure there are other ways to manipulate things, but this looks
                  > > like a pretty good thing. I'm saving it.
                  > >
                  > > Home is a binary 1/0 indicator for home/road, resp?
                  >
                  > Yes.
                  >
                  > Okay. Here are the results for 03-04. For the Matchup Probability, I
                  used
                  > the team records heading into the game. For example, for two teams
                  playing
                  > their first games of the season, I would use 0-0 records for each
                  team in my
                  > probability calculation.

                  I was curious to see how you handled the early games of the season,
                  especially the times where one team was undefeated. It looks like you
                  used Pythagorean projections, rather than real records anyway. That
                  helps. But 0-0 usually requires some other assumption, like a
                  Bayesian prior that carries through the first few games.

                  >Interestingly, this doesn't seem to affect the
                  > regression results too much. The effect of Days between games is
                  reduced in
                  > this sample. Weird.

                  Not sure what to make of that weakening of the Days. What was the R2
                  of the previous version? We may have to improve the prior matchup P
                  to get back a reasonable estimate of the value of Days. If you just
                  look at games beyond the first 20 in the season, does r2 get better
                  and does Days become more significant?

                  >
                  >
                  > PtsDiff = - 13.6 + 7.31 Home +0.000027 Distance + 18.1 WinProb + 0.722
                  > Days - 0.122 Days^2
                  >
                  > Predictor Coef SE Coef T P
                  > Constant -13.582 1.173 -11.58 0.000
                  > Home 7.3056 0.5010 14.58 0.000
                  > Distance 0.0000269 0.0003734 0.07 0.943
                  > WinProb 18.054 1.163 15.53 0.000
                  > Days 0.7221 0.7202 1.00 0.316
                  > Days^2 -0.1216 0.1138 -1.07 0.286
                  >
                  > S = 11.48 R-Sq = 16.7% R-Sq(adj) = 16.6%
                  >
                  > Analysis of Variance
                  >
                  > Source DF SS MS F P
                  > Regression 5 62072 12414 94.19 0.000
                  > Residual Error 2343 308806 132
                  > Total 2348 370877
                  >

                  DeanO

                  Dean Oliver
                  Author, Basketball on Paper
                  http://www.basketballonpaper.com
                  "Excellent writing. There are a lot of math guys who just rush from
                  the numbers to the conclusion. . .they'll tell you that Shaq is a real
                  good player but his team would win a couple more games a year if he
                  could hit a free throw. Dean is more than that; he's really
                  struggling to understand the actual problem, rather than the
                  statistical after-image of it. I learn a lot by reading him." Bill
                  James, author Baseball Abstract
                • igor eduardo küpfer
                  Replies to DanR and DeanO ... I m sorry I didn t make it clear. For the second analysis (on the 03-04 regular season results) I didn;t use Pythagorean records.
                  Message 8 of 19 , Jun 2, 2004
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                    Replies to DanR and DeanO

                    Dean Oliver wrote:

                    > I was curious to see how you handled the early games of the season,
                    > especially the times where one team was undefeated. It looks like you
                    > used Pythagorean projections, rather than real records anyway. That
                    > helps. But 0-0 usually requires some other assumption, like a
                    > Bayesian prior that carries through the first few games.

                    I'm sorry I didn't make it clear. For the second analysis (on the 03-04
                    regular season results) I didn;t use Pythagorean records. I instead used
                    each team's record to date. Two teams facing each other on the first game of
                    the season each had a 0.5 chance of winning that game, since they had
                    identical 0-0 records.

                    The results don't deviate much from my first analysis, which used season's
                    end Pythagorean Win%. I supposed this is because after the first part of the
                    season, each team's Pyth is relatively stable. I must admit to being a
                    little surprised by this, though.

                    > Not sure what to make of that weakening of the Days. What was the R2
                    > of the previous version?

                    r = 0.06 for 00-01, r = 0.03 for this season.

                    > We may have to improve the prior matchup P
                    > to get back a reasonable estimate of the value of Days. If you just
                    > look at games beyond the first 20 in the season, does r2 get better
                    > and does Days become more significant?
                    >

                    Games 2-20: r = 0.048 (p = 0.261)
                    Games 21-82: r = 0.024 (p = 0.314

                    dan_t_rosenbaum wrote:

                    > Interesting results. Here are a couple of suggestions.
                    >
                    > I would leave out the MatchupP variable, since it is a lot like the
                    > dependent variable. Including it probably increases R-squared a
                    > lot, but probably doesn't do much else. (All in all, it probably is
                    > pretty harmless, since it unlikely to be correlated with your
                    > independent variables.)
                    >
                    > Another option with your day variable is to enter it as a series of
                    > dummy variables.
                    >
                    > DAY0 - equals 1 if 0 days of rest, 0 otherwise
                    > DAY1 - equals 1 if 1 day of rest , 0 otherwise
                    > DAY2 - equals 1 if 2 days of rest, 0 otherwise
                    > DAY3 - equals 1 if 3 days of rest, 0 otherwise
                    > DAY4+ - equals 1 if 4 days or more of rest, 0 otherwise
                    >
                    > Then run the regression leaving one of those variables out.
                    >
                    > If, for example, you left DAY0 out of the regression, the DAY1
                    > coefficient would give you the effect of playing on one day's rest
                    > versus playing in a back-to-back.
                    >
                    > The DAY2 coefficent would give you the effect of playing on two
                    > days' rest versus playing in a back-to-back.
                    >
                    > The DAY3 coefficent would give you the effect of playing on three
                    > days' rest versus playing in a back-to-back.
                    >
                    > The DAY4+ coefficent would give you the effect of playing on four or
                    > more days' rest versus playing in a back-to-back.
                    >

                    Okay, I tried this. The regression outputs follow. I'm afraid that I don't
                    know how to interpret the results -- very few of the coefficients are
                    significant. (Note that I use Day1 to mean 1 day between games, ie back to
                    back -- the 1 does not mean "rest days.")

                    Ommitting Days1

                    Predictor Coef SE Coef T P
                    Constant -3.8515 0.5971 -6.45 0.000
                    Home 7.1257 0.5267 13.53 0.000
                    Distance -0.0000105 0.0003920 -0.03 0.979
                    Days2 0.5837 0.6157 0.95 0.343
                    Days3 0.3022 0.7996 0.38 0.706
                    Days4 -2.363 1.394 -1.70 0.090
                    Days5+ 0.935 1.801 0.52 0.604


                    Omitting Days2

                    Predictor Coef SE Coef T P
                    Constant -3.2678 0.5624 -5.81 0.000
                    Home 7.1257 0.5267 13.53 0.000
                    Distance -0.0000105 0.0003920 -0.03 0.979
                    Days1 -0.5837 0.6157 -0.95 0.343
                    Days3 -0.2815 0.6981 -0.40 0.687
                    Days4 -2.947 1.338 -2.20 0.028
                    Days5+ 0.351 1.758 0.20 0.842


                    Omitting Days3

                    Predictor Coef SE Coef T P
                    Constant -3.5494 0.7833 -4.53 0.000
                    Home 7.1257 0.5267 13.53 0.000
                    Distance -0.0000105 0.0003920 -0.03 0.979
                    Days1 -0.3022 0.7996 -0.38 0.706
                    Days2 0.2815 0.6981 0.40 0.687
                    Days4 -2.665 1.426 -1.87 0.062
                    Days5+ 0.633 1.824 0.35 0.729


                    Omitting Days4

                    Predictor Coef SE Coef T P
                    Constant -6.215 1.385 -4.49 0.000
                    Home 7.1257 0.5267 13.53 0.000
                    Distance -0.0000105 0.0003920 -0.03 0.979
                    Days1 2.363 1.394 1.70 0.090
                    Days2 2.947 1.338 2.20 0.028
                    Days3 2.665 1.426 1.87 0.062
                    Days5+ 3.298 2.152 1.53 0.126

                    Omitting Days5+

                    Predictor Coef SE Coef T P
                    Constant -2.917 1.799 -1.62 0.105
                    Home 7.1257 0.5267 13.53 0.000
                    Distance -0.0000105 0.0003920 -0.03 0.979
                    Days1 -0.935 1.801 -0.52 0.604
                    Days2 -0.351 1.758 -0.20 0.842
                    Days3 -0.633 1.824 -0.35 0.729
                    Days4 -3.298 2.152 -1.53 0.126


                    ed
                  • igor eduardo küpfer
                    ... http://www.shrpsports.com/nba/stand/2002.htm -- ed
                    Message 9 of 19 , Nov 2, 2004
                    • 0 Attachment
                      ivan ivan wrote:
                      > this is a simple question
                      > but i can't find it anywhere....
                      >
                      >
                      > I'm doing analysis on how a history of winning or losing affects your
                      > chances of winning at the end of close games... so does anyone know
                      > where i can standings for the 2001-2002 NBA season?
                      > i want the home and away records?
                      >

                      http://www.shrpsports.com/nba/stand/2002.htm

                      --
                      ed
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